PROJECT SUMMARY/ABSTRACT The 2017 fall Atlantic hurricane season, including Hurricane Harvey, was the most extreme in recorded history, and, in light of global climate change, a possible harbinger of future seasons to come. It is increasingly critical to understand how potentially modifiable pre-, peri-, and post-hurricane factors shape the long-term mental health of affected populations, so that we may optimize interventions to limit the ultimate impact of such storms. Individual-level experiences during and after hurricanes ? such as displacement and job loss ? shape post-hurricane mental health, but a better understanding of how these individual-level events interact with community-level factors to produce outcomes could help us to further tailor treatment approaches for individuals and communities in disaster settings. Critically, very little is known about the effects of hurricane relief efforts ? including housing and income assistance ? on longer-term outcomes. We will address these gaps using a pre-, peri-, and post-hurricane framework to organize the influences of exposure characteristics and sequencing on mental health outcomes. In our first aim, we will characterize how interactions among pre- hurricane capacities (e.g., social capital) and vulnerabilities (e.g., poor housing quality) as well as peri- hurricane stressors (e.g., power outages) and protectors (e.g., efficient government responses) ? at both individual and community levels ? shape post-hurricane depression and posttraumatic stress disorder. In our second aim, we will identify and test the effects of hypothetical interventions on post-hurricane mental health through discrete stochastic simulations, under varying profiles of pre-, peri-, and post-hurricane capacities, vulnerabilities, stressors, and protectors derived from aim 1. The primary goal of this proposed project is to build on and validate prior simulation analyses to create a set of first-in-class simulation models to identify optimal approaches to mental health services following natural disasters, and to project their public health impact. To achieve these aims, we will geographically sample and survey individuals who lived in Hurricane Harvey-affected areas of Texas about their experiences, incorporating a recall validation subsample with previously collected pre-hurricane data. We will also capitalize on archival data by collecting variables at the community level such as income inequality measures, quality of built environment, and hurricane exposure indicators, to perform multilevel analyses across varying geographic levels. Finally, we will leverage data from our de-novo survey to create synthetic populations with varying combinations of pre-, peri-, and post-event factors, and use data from an ongoing post-hurricane randomized control trial to calibrate and validate simulation models. Such in-silico experiments will shed light on the effectiveness of candidate interventions and help us to understand their potential benefits, including comparative treatment- and cost-effectiveness. This project will bring together an experienced team using novel methods to tackle an essential and timely public health problem, the implications of which may also be extended to other types of disasters and contexts.